{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        ""
      ],
      "metadata": {
        "id": "0xTDdv0aHa8K"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Transfer Learning for NLP using BERT"
      ],
      "metadata": {
        "id": "9L8DYOngGI98"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Installing Required libraries\n",
        "###Step 1.1- Please refer to the \"Technical Requirements\" section in the book for the neccessary packages to be installed. Please note that chapter has differnt Pytorch Lightning version and thus diff torch dependancies. Some functions may not work with other versions than what is tested below, so please ensure correct versions."
      ],
      "metadata": {
        "id": "RTlunyg2GTis"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "hDc6VSridGWb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e3d84106-ae13-4c67-9dcb-eaef92103357"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[K     |██████████████████████████████▎ | 834.1 MB 1.3 MB/s eta 0:00:37tcmalloc: large alloc 1147494400 bytes == 0x1ff4000 @  0x7f2e38ae9615 0x592b76 0x4df71e 0x59afff 0x515655 0x549576 0x593fce 0x548ae9 0x51566f 0x549576 0x593fce 0x548ae9 0x5127f1 0x598e3b 0x511f68 0x598e3b 0x511f68 0x598e3b 0x511f68 0x4bc98a 0x532e76 0x594b72 0x515600 0x549576 0x593fce 0x548ae9 0x5127f1 0x549576 0x593fce 0x5118f8 0x593dd7\n",
            "\u001b[K     |████████████████████████████████| 881.9 MB 20 kB/s \n",
            "\u001b[K     |████████████████████████████████| 23.3 MB 1.9 MB/s \n",
            "\u001b[K     |████████████████████████████████| 8.0 MB 34.5 MB/s \n",
            "\u001b[K     |████████████████████████████████| 2.9 MB 34.3 MB/s \n",
            "\u001b[K     |████████████████████████████████| 1.0 MB 5.5 MB/s \n",
            "\u001b[K     |████████████████████████████████| 829 kB 36.9 MB/s \n",
            "\u001b[K     |████████████████████████████████| 408 kB 40.9 MB/s \n",
            "\u001b[K     |████████████████████████████████| 596 kB 37.5 MB/s \n",
            "\u001b[K     |████████████████████████████████| 136 kB 50.0 MB/s \n",
            "\u001b[K     |████████████████████████████████| 1.1 MB 39.5 MB/s \n",
            "\u001b[K     |████████████████████████████████| 144 kB 45.7 MB/s \n",
            "\u001b[K     |████████████████████████████████| 271 kB 39.8 MB/s \n",
            "\u001b[K     |████████████████████████████████| 94 kB 3.1 MB/s \n",
            "\u001b[?25h  Building wheel for future (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[K     |████████████████████████████████| 884 kB 5.4 MB/s \n",
            "\u001b[K     |████████████████████████████████| 1.2 MB 39.0 MB/s \n",
            "\u001b[K     |████████████████████████████████| 3.0 MB 37.5 MB/s \n",
            "\u001b[K     |████████████████████████████████| 880 kB 41.9 MB/s \n",
            "\u001b[?25h  Building wheel for sacremoses (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
          ]
        }
      ],
      "source": [
        "#install correct version as defined in the Technical Req\n",
        "!pip install torch==1.10.0 torchvision==0.11.1 torchtext==0.11.0 torchaudio==0.10.0 --quiet\n",
        "!pip install pytorch-lightning==1.5.2 --quiet\n",
        "!pip install transformers==3.1.0 --quiet"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Step 1.2 Import the neccessary packages. Please refer to the section, Importing the packages in the book for further details. "
      ],
      "metadata": {
        "id": "nBZAOP_tHCud"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "uyJ2VosidNK_"
      },
      "outputs": [],
      "source": [
        "#import correct version\n",
        "import torch\n",
        "from torch import nn, optim\n",
        "from torch.utils.data import DataLoader\n",
        "from torch.utils.data import TensorDataset, DataLoader, RandomSampler, SequentialSampler\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "import pytorch_lightning as pl\n",
        "from torchmetrics.functional import accuracy\n",
        "import transformers\n",
        "from transformers import BertModel, BertConfig\n",
        "from transformers import AutoModel, BertTokenizerFast\n",
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "tMhdnQ0odO5a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "53975270-542f-46ae-d04b-3140f0806a27"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1.10.0+cu102\n",
            "3.1.0\n"
          ]
        }
      ],
      "source": [
        "#verify that correct package version are available\n",
        "print(torch.__version__)\n",
        "print(transformers.__version__)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Step 2.1 - Load the dataset- \n",
        "###Please refer to the book in section \"collecting the dataset\" for the proper way to collect the dataset and steps to import it. *"
      ],
      "metadata": {
        "id": "9xngPbKZHfCj"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "e56nHUrTdQ-Z"
      },
      "outputs": [],
      "source": [
        "#copy correct path from the book to get dataset\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "DHnmz_x7i7YS"
      },
      "outputs": [],
      "source": [
        "pub_health_train = pd.read_csv(\"PUBHEALTH/train.tsv\", sep='\\t')\n",
        "pub_health_test = pd.read_csv(\"PUBHEALTH/test.tsv\", sep='\\t')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "KULgHK7akG5l",
        "outputId": "c20ef061-6ef8-44d8-849d-c7e8a4454e34"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                           main_text    label\n",
              "0  \"Hillary Clinton is in the political crosshair...    false\n",
              "1  While the financial costs of screening mammogr...  mixture\n",
              "2  The news release quotes lead researcher Robert...  mixture\n",
              "3  The story does discuss costs, but the framing ...     true\n",
              "4  \"Although the story didn’t cite the cost of ap...     true"
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      ],
      "source": [
        "pub_health_train = pub_health_train[pub_health_train['label'] != 'snopes']\n",
        "pub_health_train = pub_health_train[['main_text','label']]\n",
        "pub_health_train = pub_health_train.dropna(subset=['main_text', 'label'])\n",
        "\n",
        "pub_health_train.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "gJuNnWyLkRJU"
      },
      "outputs": [],
      "source": [
        "pub_health_test = pub_health_test[['main_text','label']]\n",
        "pub_health_test = pub_health_test.dropna(subset=['main_text', 'label'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "Ti_pSGlFlCpy"
      },
      "outputs": [],
      "source": [
        "pub_health_train['label'] = pub_health_train['label'].map({\"true\":0, \"false\":1, \"unproven\":2, \"mixture\":3})\n",
        "pub_health_test['label'] = pub_health_test['label'].map({\"true\":0, \"false\":1, \"unproven\":2, \"mixture\":3})"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "eBxF0anhkm1i"
      },
      "outputs": [],
      "source": [
        "class HealthClaimClassifier(pl.LightningModule):\n",
        "\n",
        "    def __init__(self, max_seq_len=512, batch_size=128, learning_rate = 0.001):\n",
        "        super().__init__()\n",
        "        self.learning_rate = learning_rate\n",
        "        self.max_seq_len = max_seq_len\n",
        "        self.batch_size = batch_size\n",
        "        self.loss = nn.CrossEntropyLoss()\n",
        "\n",
        "        self.pretrain_model  = AutoModel.from_pretrained('bert-base-uncased')\n",
        "        self.pretrain_model.eval()\n",
        "        for param in self.pretrain_model.parameters():\n",
        "            param.requires_grad = False\n",
        "\n",
        "\n",
        "        self.new_layers = nn.Sequential(\n",
        "            nn.Linear(768, 512),\n",
        "            nn.ReLU(),\n",
        "            nn.Dropout(0.2),\n",
        "            nn.Linear(512,4),\n",
        "            nn.LogSoftmax(dim=1)\n",
        "        )\n",
        "\n",
        "    def prepare_data(self):\n",
        "      tokenizer = BertTokenizerFast.from_pretrained('bert-base-uncased')\n",
        "\n",
        "      tokens_train = tokenizer.batch_encode_plus(\n",
        "          pub_health_train[\"main_text\"].tolist(),\n",
        "          max_length = self.max_seq_len,\n",
        "          pad_to_max_length=True,\n",
        "          truncation=True,\n",
        "          return_token_type_ids=False\n",
        "      )\n",
        "\n",
        "      tokens_test = tokenizer.batch_encode_plus(\n",
        "          pub_health_test[\"main_text\"].tolist(),\n",
        "          max_length = self.max_seq_len,\n",
        "          pad_to_max_length=True,\n",
        "          truncation=True,\n",
        "          return_token_type_ids=False\n",
        "      )\n",
        "\n",
        "      self.train_seq = torch.tensor(tokens_train['input_ids'])\n",
        "      self.train_mask = torch.tensor(tokens_train['attention_mask'])\n",
        "      self.train_y = torch.tensor(pub_health_train[\"label\"].tolist())\n",
        "\n",
        "      self.test_seq = torch.tensor(tokens_test['input_ids'])\n",
        "      self.test_mask = torch.tensor(tokens_test['attention_mask'])\n",
        "      self.test_y = torch.tensor(pub_health_test[\"label\"].tolist())\n",
        "\n",
        "    def forward(self, encode_id, mask):\n",
        "        _, output= self.pretrain_model(encode_id, attention_mask=mask)\n",
        "        output = self.new_layers(output)\n",
        "        return output\n",
        "\n",
        "    def train_dataloader(self):\n",
        "      train_dataset = TensorDataset(self.train_seq, self.train_mask, self.train_y)\n",
        "      self.train_dataloader_obj = DataLoader(train_dataset, batch_size=self.batch_size)\n",
        "      return self.train_dataloader_obj\n",
        "\n",
        "\n",
        "    def test_dataloader(self):\n",
        "      test_dataset = TensorDataset(self.test_seq, self.test_mask, self.test_y)\n",
        "      self.test_dataloader_obj = DataLoader(test_dataset, batch_size=self.batch_size)\n",
        "      return self.test_dataloader_obj\n",
        "\n",
        "    def training_step(self, batch, batch_idx):\n",
        "      encode_id, mask, targets = batch\n",
        "      outputs = self(encode_id, mask) \n",
        "      preds = torch.argmax(outputs, dim=1)\n",
        "      train_accuracy = accuracy(preds, targets)\n",
        "      loss = self.loss(outputs, targets)\n",
        "      self.log('train_accuracy', train_accuracy, prog_bar=True, on_step=False, on_epoch=True)\n",
        "      self.log('train_loss', loss, on_step=False, on_epoch=True)\n",
        "      return {\"loss\":loss, 'train_accuracy': train_accuracy}\n",
        "\n",
        "    def test_step(self, batch, batch_idx):\n",
        "      encode_id, mask, targets = batch\n",
        "      outputs = self.forward(encode_id, mask)\n",
        "      preds = torch.argmax(outputs, dim=1)\n",
        "      test_accuracy = accuracy(preds, targets)\n",
        "      loss = self.loss(outputs, targets)\n",
        "      return {\"test_loss\":loss, \"test_accuracy\":test_accuracy}\n",
        "\n",
        "    def test_epoch_end(self, outputs):\n",
        "      test_outs = []\n",
        "      for test_out in outputs:\n",
        "          out = test_out['test_accuracy']\n",
        "          test_outs.append(out)\n",
        "      total_test_accuracy = torch.stack(test_outs).mean()\n",
        "      self.log('total_test_accuracy', total_test_accuracy, on_step=False, on_epoch=True)\n",
        "      return total_test_accuracy\n",
        "\n",
        "    def configure_optimizers(self):\n",
        "      params = self.parameters()\n",
        "      optimizer = optim.Adam(params=params, lr = self.learning_rate)\n",
        "      return optimizer"
      ]
    },
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            "Running in fast_dev_run mode: will run a full train, val, test and prediction loop using 1 batch(es).\n"
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            "/usr/local/lib/python3.7/dist-packages/transformers/tokenization_utils_base.py:1770: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n",
            "  FutureWarning,\n",
            "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
            "\n",
            "  | Name           | Type             | Params\n",
            "----------------------------------------------------\n",
            "0 | loss           | CrossEntropyLoss | 0     \n",
            "1 | pretrain_model | BertModel        | 109 M \n",
            "2 | new_layers     | Sequential       | 395 K \n",
            "----------------------------------------------------\n",
            "395 K     Trainable params\n",
            "109 M     Non-trainable params\n",
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            "439.512   Total estimated model params size (MB)\n",
            "/usr/local/lib/python3.7/dist-packages/pytorch_lightning/trainer/data_loading.py:407: UserWarning: The number of training samples (1) is smaller than the logging interval Trainer(log_every_n_steps=50). Set a lower value for log_every_n_steps if you want to see logs for the training epoch.\n",
            "  f\"The number of training samples ({self.num_training_batches}) is smaller than the logging interval\"\n"
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            "  f\"Setting `Trainer(progress_bar_refresh_rate={progress_bar_refresh_rate})` is deprecated in v1.5 and\"\n",
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            "395 K     Trainable params\n",
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            "439.512   Total estimated model params size (MB)\n",
            "/usr/local/lib/python3.7/dist-packages/pytorch_lightning/trainer/data_loading.py:111: UserWarning: The dataloader, train_dataloader, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 4 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\n",
            "  f\"The dataloader, {name}, does not have many workers which may be a bottleneck.\"\n"
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            "/usr/local/lib/python3.7/dist-packages/pytorch_lightning/trainer/trainer.py:1394: UserWarning: `.test(ckpt_path=None)` was called without a model. The best model of the previous `fit` call will be used. You can pass `test(ckpt_path='best')` to use and best model checkpoint and avoid this warning or `ckpt_path=trainer.model_checkpoint.last_model_path` to use the last model.\n",
            "  f\"`.{fn}(ckpt_path=None)` was called without a model.\"\n",
            "/usr/local/lib/python3.7/dist-packages/transformers/tokenization_utils_base.py:1770: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n",
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            "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
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            "--------------------------------------------------------------------------------\n",
            "DATALOADER:0 TEST RESULTS\n",
            "{'total_test_accuracy': 0.6095871925354004}\n",
            "--------------------------------------------------------------------------------\n"
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